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About the Role

We are looking for a Financial Engineer to join the team that builds and maintains the volatility models and exotic pricers used by our clients, including front-office trading desks that rely on our solution to risk-manage their exotics books every day. You will work on fitting implied volatility smiles across Equity and FX, improving the accuracy and stability of our equity and FX exotics pricers and their risks, and applying modern AI and machine learning techniques to speed up risk computation. Your work will directly affect the prices, Greeks and risk numbers that traders and risk managers act on, so precision, robustness and clear communication matter as much as technical depth.

Responsibilities

Build, improve and maintain analytical and semi-analytical models to fit option smile and surface data across Equity and FX.
Ensure calibrated surfaces are arbitrage-free, stable over time and robust to noisy or sparse market data.
Handle asset-class specifics: dividends and borrow for equity, delta and quote conventions for FX.
Enhance pricing models and numerical methods (Monte Carlo, PDE, semi-analytical) for equity and FX exotics such as barriers, autocallables, cliquets, basket and worst-of products.
Improve the accuracy and stability of prices and sensitivities, including smooth Greeks, convergence, handling of discontinuities and consistency between pricing and risk.
Investigate and resolve pricing and risk issues raised by traders, risk managers and clients, and explain model behaviour in practical terms.
Contribute to model validation, back-testing and benchmarking.
Leverage advanced AI techniques (e.g. neural-network pricers and calibrators, surrogate models, algorithmic differentiation, Chebyshev or regression-based approximations) to significantly accelerate Value-at-Risk (VaR) and XVA computations.
Assess the accuracy, stability and limitations of accelerated methods against reference pricers, in particular for risk and hedging use.
Use AI coding tools to speed up the development, testing, refactoring and documentation of pricing and calibration code, and share effective practices with the team.
Validate AI-generated code rigorously (unit tests, convergence checks, comparison against reference implementations, code review), so that faster delivery does not compromise numerical accuracy or model governance.
Produce rigorous analysis and comprehensive documentation of model methodologies, architectures and research findings to support transparency, model governance and knowledge transfer across the organisation.
Extract insights from complex, high-volume market and risk datasets to inform model development, diagnose model behaviour and shape business strategy.
Provide clear explanations of model behaviour (including AI-based models), conduct scenario analysis and deliver actionable analytics to stakeholders such as front-office traders, risk managers, sales and technology teams.

Requirements

Proven experience as a quant in derivatives pricing or modelling at a bank, hedge fund, vendor or consultancy.
Strong knowledge of option pricing theory, stochastic calculus and volatility modelling (local vol, stochastic vol, local-stochastic vol, smile dynamics).
Hands-on experience calibrating volatility surfaces and building or validating exotic option pricers in equity and/or FX.
Solid grasp of numerical methods: Monte Carlo, finite-difference/PDE, optimisation and interpolation.
Strong programming skills in Python and C++ (or a comparable compiled language), with good software engineering practices (testing, version control, code review).
Proficiency with AI coding assistants and agentic tools (e.g. Claude, Codex, Copilot), with a track record of using them to deliver code, research and documentation significantly faster, while keeping the rigour expected of production quant libraries.
Ability to communicate complex quantitative topics clearly to both technical and non-technical audiences.
Rigour and attention to detail, with a focus on numerical stability and risk accuracy.
Curiosity and willingness to explore new techniques while understanding their limits.
Ownership of problems from diagnosis through to production delivery.
A collaborative approach and the ability to work with traders, risk managers and developers.

Skills

Python
C++
AI coding tools
Option pricing theory
Stochastic calculus
Volatility modelling
Numerical methods
Monte Carlo
PDE
Finite-difference
Optimization
Interpolation
AI coding assistants
Agentic tools
Claude
Codex
Copilot

Location

Global

Work Type

Full-time

Experience Level

Proven experience

Education Level

Master’s or PhD in Mathematics, Physics, Financial Engineering, Computer Science or a related quantitative field.

About the Company

Intercontinental Exchange (NYSE:ICE) is a Fortune 500 company operating a leading network of global futures, equity, and equity options exchanges, alongside world-class clearing and data services spanning financial and commodity markets.
We place the needs of our customers and global market participants at the forefront of everything we do.
As a high-growth organisation, we pioneered transparency and risk management in global derivatives markets.
Our team comprises engineers, strategists, and problem-solvers who continuously innovate technology solutions to address complex market structure challenges.
The ICE Data Derivatives business provides market data, valuation, and analytics services across all OTC derivative asset classes to a global customer base.